This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!. EMNLP'2023 (Findings).

If you have any question about this work, feel free to contact [email protected]

We develop filter-then-rerank paradigm to solve IE tasks effectively and efficiently. Combining the merits of both Small Language Models (SLMs) and Large Language Models (LLMs), this paradigm includes two main components:

• Filter: A small LM. It initially predicts and determines the difficulty of each sample. In our work, the difficulty is determined by the maximum predicted probability $s$ among all candidate labels. For easy samples ($s > \tau$, where $\tau$ is a threshold), we retain the predicted results from LLMs. For hard samples ($s < \tau$), we provide the short-list candidate (top-K likely labels) to reranker for final decision.
• Reranker: A large LM. We convert each difficult sample with their candidate answers to a multi-choice question. Given several demonstrations, we ask the reranker to analyze and answer this question.

• 2024.04.12: Support open-source LLM-rerankerss now. Test on vicuna-7b-v1.5 and Mistral-7B-Instruct-v0.2
• 2024.02.29: Code Release. Sorry for the delay.

Environment

• python 3.8.13
• Pytorch 1.10.1
• openai 0.27.0
• Transformers 4.36.0
• backoff
• sentencepiece

OpenAI API key

It you use GPT-4 as the reranker, please set up your openAI API key by:

export OPENAI_API_KEY=[your api key]

Results from the filter (SLM)

Unfortunately, we only provide the SLM's preliminary predictions of FewNERD dataset. We are not granted to release SLM's predictions on TACREV and ACE05 because they are non-public datasets. For reproduce the results of these two datasets, we suggest you:

1. Run SLM-based methods (for example, vanilla fine-tuning) on these datasets.

2. Save the results in ./SLM_results. For TACREV (relation extraction task) and ACE05 (event detection task), please save the predictions as the format shown in ./SLM_results/examples_re_tacrev.json and ./SLM_results/examples_ed_ace.json, respectively.

FewNERD

MODEL_NAME=[gpt-4|Mistral-7B-Instruct-v0.2|...] bash ./scripts/run_ner_fewnerd.sh

TACREV

# To run this command, you shall firstly generate and save corresponding SLM's predictions.
# Then complete the env variables SLM_RES_PATH and OUT_PATH in the script.
MODEL_NAME=[gpt-4|Mistral-7B-Instruct-v0.2|...] bash ./scripts/run_re_tacrev.sh

ACE05

# To run this command, you shall firstly generate and save corresponding SLM's predictions.
# Then complete the env variables SLM_RES_PATH and OUT_PATH in the script.
MODEL_NAME=[gpt-4|Mistral-7B-Instruct-v0.2|...] bash ./scripts/run_ed_ace.sh

Please cite our paper if it is helpful for your work:

@misc{ma2023large,
      title={Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!}, 
      author={Yubo Ma and Yixin Cao and YongChing Hong and Aixin Sun},
      year={2023},
      eprint={2303.08559},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}